class05.R

#Class 05: Data Visualization

#Today we are going to use ggplot2 package

#First we need to load the package!
#install.packages("ggplot2") - this is a one-time only installation!
library(ggplot2)

#We will use this in-built "cars" dataset first
head(cars)

##   speed dist
## 1     4    2
## 2     4   10
## 3     7    4
## 4     7   22
## 5     8   16
## 6     9   10

#All ggplots have at least 3 layers;
#data + aes + geoms
ggplot(data=cars) + 
  aes(x=speed, y=dist) +
  geom_point() +
  geom_smooth(method="lm") +
  labs(title="Stopping Distance of Old Cars",
       x="Speed (MPH)", 
       y="Stopping Distance (ft)")

## `geom_smooth()` using formula 'y ~ x'

#Grey area around line = SEM
#Side note: ggplot is not the only graphics system
#A very popular one is good old "base" R graphics
plot(cars)

plot(cars$speed, cars$speed)

#Follow this same process as directed in the worksheet for lab 5. Now, we will be looking at gene expression changes upon drug treatment
url <- "https://bioboot.github.io/bimm143_S20/class-material/up_down_expression.txt"
genes <- read.delim(url)
head(genes)

##         Gene Condition1 Condition2      State
## 1      A4GNT -3.6808610 -3.4401355 unchanging
## 2       AAAS  4.5479580  4.3864126 unchanging
## 3      AASDH  3.7190695  3.4787276 unchanging
## 4       AATF  5.0784720  5.0151916 unchanging
## 5       AATK  0.4711421  0.5598642 unchanging
## 6 AB015752.4 -3.6808610 -3.5921390 unchanging

p <- ggplot(data=genes) +
  aes(x=Condition1, y=Condition2, col=State) +
  geom_point() + 
  scale_color_manual(values=c("blue", "gray", "red")) +
  labs(title="Gene Expression Changes Upon Drug Treatment",
       x="Control (No Drug)",
       y="Drug Treatment")
#Question: How many genes are in this dataset?
nrow(genes)

## [1] 5196

#Question: How many genes are "up"?
table(genes$State)

## 
##       down unchanging         up 
##         72       4997        127

#Question: What % are up?
table(genes$State)/nrow(genes)

## 
##       down unchanging         up 
## 0.01385681 0.96170131 0.02444188

#Question: How do we report this as a percentage?
table(genes$State)/nrow(genes) * 100

## 
##       down unchanging         up 
##   1.385681  96.170131   2.444188

#Question: How do we round off to a specific number of sig figs?
round(table(genes$State)/nrow(genes) * 100, 3)

## 
##       down unchanging         up 
##      1.386     96.170      2.444

#See lines 29-35 to make a figure

#General notes on making the figure:
p <- ggplot(genes) +
  aes(x=Condition1, y=Condition2, col=State) +
  geom_point()

p

# I like it but not the default colors, let's change them
p + scale_color_manual(values=c("blue", "gray", "red"))

#Section of lab: Going Further
library(gapminder)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

gapminder_2007 <- gapminder %>% filter(year==2007)

#Let's make another figure - first, a scatterplot!
ggplot(gapminder_2007) +
  aes(x=gdpPercap, y=lifeExp) +
  geom_point(alpha=0.5)

#The "alpha" value will make the points on the scatterplot more transparent
#To add even more complexity to the plot, we will add the following to our calls:
ggplot(gapminder_2007) +
  aes(x=gdpPercap, y=lifeExp, color=continent, size=pop) +
  geom_point(alpha=0.5)

#Now try this...
ggplot(gapminder_2007) +
  aes(x=gdpPercap, y=lifeExp, color=pop) +
  geom_point(alpha=0.8)

#Now try this...
ggplot(gapminder_2007) +
  aes(x=gdpPercap, y=lifeExp, size=pop) +
  geom_point(alpha=0.5)

#To add scaling information:
ggplot(gapminder_2007) +
  aes(x=gdpPercap, y=lifeExp, size=pop) +
  geom_point(alpha=0.5) +
  scale_size_area(max_size = 10)

#Now, let's try putting this all together to generate a new figure:
gapminder_1957 <- gapminder %>% filter(year==1957)
ggplot(gapminder_1957)

ggplot(gapminder_1957) +
  aes(x=gdpPercap, y=lifeExp, color=continent) +
  geom_point()

ggplot(gapminder_1957) +
  aes(x=gdpPercap, y=lifeExp, color=continent, size=pop) +
  geom_point()

ggplot(gapminder_1957) +
  aes(x=gdpPercap, y=lifeExp, color=continent) +
  geom_point(alpha=0.7) +
  scale_size_area(max_size = 15)

ggplot(gapminder_1957) +
  aes(x=gdpPercap, y=lifeExp, color=continent) +
  geom_point(alpha=0.7) +
  scale_size_area(max_size = 15) +
  facet_wrap(~year)

#Let's make a new plot of year vs. lifeExp
ggplot(gapminder) +
  aes(x=year, y=lifeExp, col=continent) +
  geom_jitter(width=0.3,alpha=0.4) +
  geom_boxplot(aes(group=year, alpha=0.2))

ggplot(gapminder) +
  aes(x=year, y=lifeExp, col=continent) +
  geom_jitter(width=0.3,alpha=0.4) +
  geom_violin(aes(group=year), alpha=0.2,
                  draw_quantiles=0.5)

#Install the plotly
#Install.packages("plotly")
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

ggplotly()